Economic Decisions with Ambiguous Outcome Magnitudes Vary with Low and High Stakes but Not Trait Anxiety or Depression
Abstract
Most of life’s decisions involve risk and uncertainty regarding whether reward or loss will follow. Decision makers often face uncertainty not only about the likelihood of outcomes (what are the chances that I will get a raise if I ask my supervisor? What are the chances that my supervisor will be upset with me for asking?) but also the magnitude of outcomes (if I do get a raise, how large will it be? If my supervisor gets upset, how bad will the consequences be for me?). Only a few studies have investigated economic decision making with ambiguous likelihoods, and even fewer have investigated ambiguous outcome magnitudes. In the present report, we investigated the effects of ambiguous outcome magnitude, risk, and gains/losses in an economic decision-making task with low stakes (Study 1; $3.60–$5.70; N = 367) and high stakes (Study 2; $6–$48; N = 210) using a within-subjects design. We conducted computational modeling to determine individuals’ preferences/aversions for ambiguous outcome magnitudes, risk, and gains/losses. We additionally investigated the association between trait anxiety and trait depression and decision-making parameters. Our results show that increasing stakes increased ambiguous gain aversion and unambiguous risk aversion but increased ambiguous sure loss preference; participants also became more averse to ambiguous sure gains relative to unambiguous risky gains. There were no significant effects of trait anxiety or trait depression on economic decision making. Our results suggest that as stakes increase, people tend to avoid uncertainty in the gain domain (especially ambiguous gains) but prefer ambiguous vs unambiguous sure losses.
Copyright and License
© 2021 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/ licenses/by/4.0/. Computational Psychiatry is a peer-reviewed open access journal published by Ubiquity Press.
Funding
This research was funded by the Russell Sage Foundation (#1912–20003) and the California Institute of Technology Linde Institute Center for Theoretical and Experimental Social Sciences.
Contributions
Tomislav D. Zbozinek and Caroline J. Charpentier contributed equally to this work and are co-first authors.
Data Availability
Please see our GitHub repository for the data and code to replicate our computational modeling, inferential statistics, and figures: https://github.com/tzbozinek/economic-decision-making-ambiguity.
Conflict of Interest
The authors have no competing interests to declare.
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Additional details
- PMCID
- PMC11104296
- Russell Sage Foundation
- 1912–20003
- California Institute of Technology
- Linde Institute Center for Theoretical and Experimental Social Sciences